Kernel/feature Selection for Support Vector Machines

Abstract

Support Vector Machines are classifiers with architectures determined by kernel functions. In these proceedings we propose a method for selecting the best SVM kernel for a given classification problem. Our method searches for the best kernel by remapping the data via a kernel variant of the classical Gram-Schmidt orthonormalization procedure then using… (More)

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Cite this paper

@inproceedings{Martin2005KernelfeatureSF, title={Kernel/feature Selection for Support Vector Machines}, author={Shawn Martin and Michael Kirby and Rick Miranda}, year={2005} }